Meta-learning assumes access to a distribution \(\mathcal {P(T)}\)  over tasks.
The goal is to utilize tasks \(T_{i} \sim \mathcal {P(T)}\)  sampled from this distribution to train a learning procedure that generalizes to unseen tasks \(T^{\prime }\sim \mathcal {P(T)}\)  from the distribution.
Supervised meta-learning often utilizes a fixed task dataset to create \(\mathcal {P(T)}\)  by sub-sampling from all class labels [1]}.
[2]} sought to provide an unsupervised approach that proposes tasks from unlabelled data.
The resulting Subset Masked Language Modeling Tasks (SMLMT) approach proposes self-supervised tasks to enable meta-learning and improves few-shot learning across a diverse set of classification tasks.
